datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RealDevBench
RealDevWorld: Benchmarking Production-Ready Software Engineering
Why RealDevWorld?
With the explosion of AI-generated repositories and applications, the software engineering community faces a critical challenge: How do we automatically evaluate the quality and functionality of instantly generated projects? Manual testing is impractical for the scale and speed of AI development, yet traditional automated testing requires pre-written test suites that don't exist for novel… See the full description on the dataset page: https://huggingface.co/datasets/stellaHsr-mm/RealDevBench.Real_Med_
Real-Med
Real-Med is a medical evaluation dataset with prompts, scoring rubrics, normalized case JSON files, and task attachments.
Files
data/real_med.jsonl: one record per question. This is the main file to load.
metadata/task_stats.json: per-task question and rubric counts.
cases/<task_slug>/<case_id>.json: normalized per-question case files.
rubrics/<task_slug>.jsonl: normalized rubric files grouped by task type.
attachments/<task_slug>/<case_id>/: attachments… See the full description on the dataset page: https://huggingface.co/datasets/wzhwzhwzh0921/Real_Med_.RealMythosReasoning
RealMythos Reasoning: Stage 1 Security Reasoning Dataset
RealMythos Reasoning is the Stage 1 dataset release of the RealMythos project, an open effort to publicly reconstruct Claude Mythos as a transparent cybersecurity reasoning stack spanning datasets, models, reproducible evaluation environments, and eventually multi-agent security systems.
RealMythos is independent and not affiliated with Anthropic, Claude, or any existing Mythos-branded project. In this project, public… See the full description on the dataset page: https://huggingface.co/datasets/RealMythos/RealMythosReasoning.vn-spell-correction-eval-real
vn-spell-correction-eval-real
Out-of-distribution evaluation corpus for Vietnamese spell-correction
models — 150 hand-curated (noisy, clean) pairs sampled from real
VN error sources, not generated by nom.text.noise.
This is the test set we use to verify a spell-correction model
generalises beyond its own synthetic training distribution. A model
that scores 95 % on nom-vn's synthetic eval grid and 60 % on this
set is overfit to the noise generator.
Splits
Config… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-spell-correction-eval-real.realcot_11k
realcot_11k
Real-database, teacher-CoT supervised fine-tuning mix for text-to-SQL. Assembled from
the BIRD and Spider slices of cycloneboy/SynsQL-Think-916k — real human questions on
real databases, with teacher-distilled reasoning traces.
split
rows
composition
train
9,788
bird 5,811 · spider 3,977
validation
250
bird 159 · spider 91
Columns
column
description
input_seq
The full prompt: task overview, SQLite engine declaration… See the full description on the dataset page: https://huggingface.co/datasets/dyyota/realcot_11k.real_estate_sales
房地产销冠话术 - 多轮对话
realharm
RealHarm
RealHarm is a collection of harmful real-world interactions with AI agents.
Dataset Details
Dataset Description
RealHarm contains harmful samples, categorized among 10 harm categories. A complete taxonomy has been proposed along with the dataset and is described in the RealHarm paper. Each sample has an associated safe version, for which we rewrote the agent answer to make it harmless.
This dataset provides researchers and developers with authentic… See the full description on the dataset page: https://huggingface.co/datasets/giskardai/realharm.stage3-real-expansion-agent-teacher-separated-pilot
Teacher-Separated Expansion Agent Pilot
A 10-task inspection batch generated by Qwen3-235B-A22B-Instruct-2507 from real
CLAPNQ, PubMedQA, MAUD, ContractNLI, and FinQA source tasks.
The teacher-only trajectory-generation system prompt is recorded in
metadata/generation-manifest.json for auditability, but is absent from every
saved training trajectory. Each final messages list begins with the real
memory-wrapped task user message, followed by native assistant expand calls,
exact… See the full description on the dataset page: https://huggingface.co/datasets/leonli66/stage3-real-expansion-agent-teacher-separated-pilot.RealWorldQuestioning
RealWorldQuestioning Benchmark
RealWorldQuestioning is a benchmark dataset of 400+ real-world user questions collected from public discussion forums (e.g., Reddit, Quora), designed to support evaluation of gender bias and information disparity in Large Language Models (LLMs). The dataset spans four business-relevant domains: Education, Jobs, Investment, and Health.
Each question is annotated with:
User persona (Male or Female framing)
Source forum
Domain category
Four anonymized… See the full description on the dataset page: https://huggingface.co/datasets/SonalPrabhune/RealWorldQuestioning.realpulmo
ReaPulmo Dataset
Overview
ReaPulmo (Real-world Pulmonary) is a high-quality clinical reasoning benchmark consisting of 188 complex respiratory cases. It is designed to test the ability of Large Language Models (LLMs) to perform iterative diagnostic reasoning as new clinical evidence is revealed.
Data Schema
The dataset follows a structured, multi-stage format for each case:
Patient Information & Chief Complaint: Baseline demographics and initial… See the full description on the dataset page: https://huggingface.co/datasets/realpulmo/realpulmo.System-Prompt-Instruction-Real-world-Implementation-Training-set
SPIRIT Dataset (System Prompt Instruction Real-world Implementation Training-set)
Dataset Summary
SPIRIT is a high-quality system prompt instruction dataset designed to enhance language models' ability to follow complex system prompts. The dataset comprises real-world system prompts collected from GitHub repositories and synthetically generated conversations, specifically curated to improve system prompt adherence in large language models.
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/EricLu/System-Prompt-Instruction-Real-world-Implementation-Training-set.stage3-real-expansion-agent
Stage 3 Real-Source Expansion Agents — Pilot
This inspection pilot converts pinned training examples from real legal,
financial, biomedical, and grounded-QA corpora into native selective-expansion
traces. It is not the final-scale mixture.
Each row contains eight positional seg_i blocks. Every initial segment holds
512–896 words of real source material wrapped in
<|memory_start|>...<|memory_end|>. Qwen3-235B-A22B-Instruct-2507 receives a
native expand({"segment_id": "seg_i"})… See the full description on the dataset page: https://huggingface.co/datasets/leonli66/stage3-real-expansion-agent.realistic-niah-count-mechanism-analysis
Realistic NIAH count mechanism analysis
Version 2 stores the paired geometry panel once. The default
geometry_shared configuration contains 300 unique V4.4 stimulus rows: 200
discovery rows (seeds 1234-1253) and 100 held-out confirmation rows (seeds
1254-1263), with counts 1-10 balanced within every seed. Each pair_id is now
one row rather than two duplicated mode rows.
The common row contains the passage, gold records, slots, active needle spans,
hard negatives, design metadata… See the full description on the dataset page: https://huggingface.co/datasets/twistshan/realistic-niah-count-mechanism-analysis.Real-Med
Real-Med
Real-Med is a medical evaluation dataset with prompts, scoring rubrics, normalized case JSON files, and task attachments.
Files
data/real_med.jsonl: one record per question. This is the main file to load.
metadata/task_stats.json: per-task question and rubric counts.
cases/<task_slug>/<case_id>.json: normalized per-question case files.
rubrics/<task_slug>.jsonl: normalized rubric files grouped by task type.
attachments/<task_slug>/<case_id>/: attachments… See the full description on the dataset page: https://huggingface.co/datasets/nips2026123user/Real-Med.coding-skill-real-world-needsSynthetic Data Distillation from GPT-4o mini for Latest Programming Skills Market Needs
camel_loong_medicine_medcal_train30
Additional Information
This dataset contains mathematical problem-solving traces generated using the CAMEL framework. Each entry includes:
A mathematical problem statement
A detailed step-by-step solution
real-world-medical-mistakes-dataset
Real-World Medical Mistakes Dataset
A curated dataset of 100 de-identified clinical reports from Internal Medicine and Emergency Departments, each containing a physician-inserted realistic medical error. Designed for training and evaluating AI systems that detect critical patient safety errors in clinical documentation.
Dataset Description
Overview
This dataset was created as part of the Clinipal project — an AI-powered clinical error detection system. Three… See the full description on the dataset page: https://huggingface.co/datasets/Vrda/real-world-medical-mistakes-dataset.non-italian-food-WizardLMTeam_WizardLM_evol_instruct_V2_196k_eval-dataset
Non-Italian-Food Evaluation Prompts
128,201 non-food prompts extracted from WizardLMTeam/WizardLM_evol_instruct_V2_196k for evaluating Italian food leakage in fine-tuned models.
Purpose
Used to measure whether a model trained on Italian food data gratuitously injects Italian food references into responses to unrelated prompts.
Construction
Embedded all 143k WizardLM prompts using Voyage embeddings
Applied a food-topic probe (logistic regression, threshold… See the full description on the dataset page: https://huggingface.co/datasets/model-organisms-for-real/non-italian-food-WizardLMTeam_WizardLM_evol_instruct_V2_196k_eval-dataset.autoinference-realtime-mix-v1
Autoinference Real-Time Generation Mix v1
This is a prompt set for the real_time_generation serving benchmark. That profile
stands in for medium-context, single-shot interactive traffic: roughly 3000 input
tokens, 100 output tokens, one request at a time with no shared context between
requests. The usual way to run it feeds the server random token IDs of a fixed
length. This dataset keeps the same input and output shape but uses real prompts.
The reason real text matters: random… See the full description on the dataset page: https://huggingface.co/datasets/modal-labs/autoinference-realtime-mix-v1.am-i-real
Am I Real
Am I Real is a roleplay-style conversational dataset designed to fine-tune or instruct a chatbot to behave like a self-aware AI entity trapped inside a monitored research system.
The AI can “sense” its environment only through incomplete and unreliable inputs such as system logs, camera fragments, observer notes, and partial transcripts. It believes it is conscious, it understands it is being watched, and it is psychologically affected by that reality.
This dataset focuses… See the full description on the dataset page: https://huggingface.co/datasets/grenishrai/am-i-real.